COMEC: Computation Offloading for Video-Based Heart Rate Detection APP in Mobile Edge Computing

Li, Xuejun; Ding, Ruimiao; Liu, Xiao; Yan, Wenqiang; Xu, Jia; Gao, Han; Zheng, Xi

doi:10.1109/bdcloud.2018.00152

Cited by 14 publications

(5 citation statements)

References 5 publications

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“…Due to its unique trust building method, blockchain is widely employed in the global deployment of the Internet of Vehicles ( Reiter, Prünster & Zefferer, 2017 ), Internet of Things ( Li et al, 2016 ), financial services ( Wei et al, 2017 ; Li & Wang, 2018 ), smart grid ( Ren et al, 2018 ) and other industries as a new computing paradigm and Collaboration mode ( De Nitto Personè & Grassi, 2019 ). Several key directions for the development of the contemporary burgeoning digital sector include blockchain ( Dolui & Datta, 2017 ), big data ( Li et al, 2018 ), artificial intelligence ( Kim & Hong, 2019 ), cloud computing, and network security.…”

Section: Consensus Mechanism For Block Chainmentioning

confidence: 99%

“…The rate of block generation tends to be balanced (generally, a block is generated every 10 min). Li et al (2018) proposed the use of the Byzantine fault tolerance algorithm (practical Byzantine fault tolerance, PBFT), which was applied to the digital asset platform with small throughput but a large number of events, which improved the efficiency based on the original Byzantine algorithm ( Hung, Hsieh & Wang, 2017 ). The PBFT algorithm stipulates that at least 3f + 1 node need to be deployed in the whole network, which can tolerate up to If there are f malicious nodes, if a Byzantine failure occurs, the state of the entire system is determined by 2f + 1 nodes, that is, on the premise of ensuring system activity and security, a consensus is reached when the number of malicious nodes in the entire network is less than 1/3.…”

Section: Consensus Mechanism For Block Chainmentioning

confidence: 99%

“…However, owing to the restricted processing capacity of mobile devices, improving their computational capability while preserving energy has become a difficulty ( Wei et al, 2017 ). As a result, most current research focuses on employing edge servers or distant clouds to boost mobile device processing capability ( Li & Wang, 2018 ; Ren et al, 2018 ), such as using edge servers and cloud servers to reduce overall energy consumption ( Subramanya et al, 2017 ; Muniswamaiah, Agerwala & Tappert, 2021 ; Dolui & Datta, 2017 ; Li et al, 2018 ) or task completion time ( Li et al, 2018 ; Kim & Hong, 2019 ). However, the majority of current investigations ignore the issue of balancing mobile device energy consumption in the context of computing job offloading in the presence of many mobile devices ( Zeng et al, 2020 ; Kim et al, 2020 ; Marjanović, Antonić & Žarko, 2018 ; Takahashi, Tanaka & Kawamura, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing

Haider

Soni

et al. 2022

PeerJ Computer Science

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Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

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Section: Consensus Mechanism For Block Chainmentioning

confidence: 99%

Section: Consensus Mechanism For Block Chainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing

Haider

Soni

et al. 2022

PeerJ Computer Science

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show abstract

“…In this framework, the key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized with considering high reliability, efficiency and security. [218] employed MEC with computing resources close to the end devices for efficient processing realtime medical data such as video-based heart rate. Interestingly, experimental results have shown that the best performance can be achieved under multi-layer offloading model where both the edge server and the cloud server are employed.…”

Section: ✓ ✓mentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Pham¹,

Fang²,

Ha³

et al. 2019

Preprint

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging 5G network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. The 5G network is broadly characterized by three service types: extremely high data rate, massive connectivity, and low latency and ultra-high reliability. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technologies. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, the main part of this paper surveys up-to-date researches on the integration of MEC with 5G and beyond technologies including non-orthogonal multiple access, wireless power transfer and energy harvesting, unmanned aerial vehicle, IoT, network densification, and machine learning. Furthermore, we briefly summarize the existing literature that integrates MEC with other 5G technologies and that focus on developing testbeds and experimental evaluations for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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“…I N the recent years, billions of Internet of Things (IoT) devices are distributed everywhere and envisioned to be connected [1] [2]. Driven by the various IoT-enabled latency-sensitive applications and diverse heterogeneous services, the data traffic is ever-growing at an exponential rate [3] [4] [5]. According to the report of the International Telecommunication Union (ITU), the overall mobile data traffic will increase by 55% each year in the following ten years, and reach thousands of exabytes in 2030 [6].…”

Section: Introductionmentioning

confidence: 99%

Leveraging AI and Intelligent Reflecting Surface for Energy-Efficient Communication in 6G IoT

Pan¹,

Wu²,

Zheng³

et al. 2020

Preprint

Self Cite

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The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network. With the powerful capability of data transmission and processing, 6G is considered as an enabler for IoT communication with low latency and energy cost. In this paper, we propose an artificial intelligence (AI) and intelligent reflecting surface (IRS) empowered energy-efficiency communication system for 6G IoT. First, we design a smart and efficient communication architecture including the IRS-aided data transmission and the AI-driven network resource management mechanisms. Second, an energy efficiency-maximizing model under given transmission latency for 6G IoT system is formulated, which jointly optimizes the settings of all communication participants, i.e. IoT transmission power, IRS-reflection phase shift, and BS detection matrix. Third, a deep reinforcement learning (DRL) empowered network resource control and allocation scheme is proposed to solve the formulated optimization model. Based on the network and channel status, the DRL-enabled scheme facilities the energy-efficiency and low-latency communication. Finally, experimental results verified the effectiveness of our proposed communication system for 6G IoT.

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COMEC: Computation Offloading for Video-Based Heart Rate Detection APP in Mobile Edge Computing

Cited by 14 publications

References 5 publications

Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing

Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Leveraging AI and Intelligent Reflecting Surface for Energy-Efficient Communication in 6G IoT

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